{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "import talib as ta\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "\n",
    "pro = ts.pro_api('0ba8feef618e5db7b1ebb65538fe51e4aef69fb3cbf709d44128f313')\n",
    "\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "rb201 = 'rb2201.DCE'\n",
    "rb205 = 'rb2205.DCE'\n",
    "df201 = pro.fut_daily(ts_code=rb201, start_date='20180101', end_date='20211231')\n",
    "df205 = pro.fut_daily(ts_code=rb205, start_date='20180101', end_date='20211231')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>pre_settle</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>settle</th>\n",
       "      <th>change1</th>\n",
       "      <th>change2</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>oi</th>\n",
       "      <th>oi_chg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-12-13</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>4770.0</td>\n",
       "      <td>4865.0</td>\n",
       "      <td>4806.0</td>\n",
       "      <td>4956.0</td>\n",
       "      <td>4773.0</td>\n",
       "      <td>4931.0</td>\n",
       "      <td>4869.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>202420.0</td>\n",
       "      <td>985778.21</td>\n",
       "      <td>121217.0</td>\n",
       "      <td>-8017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-14</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>4931.0</td>\n",
       "      <td>4869.0</td>\n",
       "      <td>4880.0</td>\n",
       "      <td>4990.0</td>\n",
       "      <td>4836.0</td>\n",
       "      <td>4988.0</td>\n",
       "      <td>4896.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>189114.0</td>\n",
       "      <td>926078.32</td>\n",
       "      <td>108045.0</td>\n",
       "      <td>-13172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-15</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>4988.0</td>\n",
       "      <td>4896.0</td>\n",
       "      <td>4987.0</td>\n",
       "      <td>4987.0</td>\n",
       "      <td>4878.0</td>\n",
       "      <td>4909.0</td>\n",
       "      <td>4937.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>151256.0</td>\n",
       "      <td>746789.75</td>\n",
       "      <td>94025.0</td>\n",
       "      <td>-14020.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-16</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>4909.0</td>\n",
       "      <td>4937.0</td>\n",
       "      <td>4909.0</td>\n",
       "      <td>4983.0</td>\n",
       "      <td>4881.0</td>\n",
       "      <td>4968.0</td>\n",
       "      <td>4930.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>97629.0</td>\n",
       "      <td>481391.22</td>\n",
       "      <td>66754.0</td>\n",
       "      <td>-27271.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-17</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>4968.0</td>\n",
       "      <td>4930.0</td>\n",
       "      <td>4967.0</td>\n",
       "      <td>5030.0</td>\n",
       "      <td>4937.0</td>\n",
       "      <td>4961.0</td>\n",
       "      <td>4985.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55550.0</td>\n",
       "      <td>276921.20</td>\n",
       "      <td>52615.0</td>\n",
       "      <td>-14139.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ts_code  pre_close  pre_settle    open    high     low   close  \\\n",
       "trade_date                                                                      \n",
       "2021-12-13  EG2201.DCE     4770.0      4865.0  4806.0  4956.0  4773.0  4931.0   \n",
       "2021-12-14  EG2201.DCE     4931.0      4869.0  4880.0  4990.0  4836.0  4988.0   \n",
       "2021-12-15  EG2201.DCE     4988.0      4896.0  4987.0  4987.0  4878.0  4909.0   \n",
       "2021-12-16  EG2201.DCE     4909.0      4937.0  4909.0  4983.0  4881.0  4968.0   \n",
       "2021-12-17  EG2201.DCE     4968.0      4930.0  4967.0  5030.0  4937.0  4961.0   \n",
       "\n",
       "            settle  change1  change2       vol     amount        oi   oi_chg  \n",
       "trade_date                                                                    \n",
       "2021-12-13  4869.0     66.0      4.0  202420.0  985778.21  121217.0  -8017.0  \n",
       "2021-12-14  4896.0    119.0     27.0  189114.0  926078.32  108045.0 -13172.0  \n",
       "2021-12-15  4937.0     13.0     41.0  151256.0  746789.75   94025.0 -14020.0  \n",
       "2021-12-16  4930.0     31.0     -7.0   97629.0  481391.22   66754.0 -27271.0  \n",
       "2021-12-17  4985.0     31.0     55.0   55550.0  276921.20   52615.0 -14139.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['trade_date'] = df['trade_date'].apply(lambda x: datetime.strptime(x,'%Y%m%d'))\n",
    "df = df.set_index('trade_date')\n",
    "df = df.sort_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>pre_settle</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>settle</th>\n",
       "      <th>change1</th>\n",
       "      <th>change2</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>oi</th>\n",
       "      <th>oi_chg</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ts_code, trade_date, pre_close, pre_settle, open, high, low, close, settle, change1, change2, vol, amount, oi, oi_chg]\n",
       "Index: []"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df201.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>pre_settle</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>settle</th>\n",
       "      <th>change1</th>\n",
       "      <th>change2</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>oi</th>\n",
       "      <th>oi_chg</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [ts_code, trade_date, pre_close, pre_settle, open, high, low, close, settle, change1, change2, vol, amount, oi, oi_chg]\n",
       "Index: []"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df205.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>oi</th>\n",
       "      <th>oi_chg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-27</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-28</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.44</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-29</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>4547.0</td>\n",
       "      <td>4547.0</td>\n",
       "      <td>4462.0</td>\n",
       "      <td>4462.0</td>\n",
       "      <td>4504.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-01</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>4462.0</td>\n",
       "      <td>4504.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>4615.0</td>\n",
       "      <td>4615.0</td>\n",
       "      <td>4623.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.25</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-02</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>4615.0</td>\n",
       "      <td>4623.0</td>\n",
       "      <td>4660.0</td>\n",
       "      <td>4660.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>4641.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.92</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-25</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>5216.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>5216.0</td>\n",
       "      <td>5353.0</td>\n",
       "      <td>5190.0</td>\n",
       "      <td>5267.0</td>\n",
       "      <td>5273.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>373413.0</td>\n",
       "      <td>1969348.48</td>\n",
       "      <td>203000.0</td>\n",
       "      <td>17181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-26</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>5267.0</td>\n",
       "      <td>5273.0</td>\n",
       "      <td>5267.0</td>\n",
       "      <td>5270.0</td>\n",
       "      <td>5055.0</td>\n",
       "      <td>5055.0</td>\n",
       "      <td>5154.0</td>\n",
       "      <td>-218.0</td>\n",
       "      <td>-119.0</td>\n",
       "      <td>275188.0</td>\n",
       "      <td>1418459.77</td>\n",
       "      <td>179694.0</td>\n",
       "      <td>-23306.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-29</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>5055.0</td>\n",
       "      <td>5154.0</td>\n",
       "      <td>4986.0</td>\n",
       "      <td>5076.0</td>\n",
       "      <td>4906.0</td>\n",
       "      <td>4929.0</td>\n",
       "      <td>4989.0</td>\n",
       "      <td>-225.0</td>\n",
       "      <td>-165.0</td>\n",
       "      <td>327885.0</td>\n",
       "      <td>1636140.32</td>\n",
       "      <td>200870.0</td>\n",
       "      <td>21176.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>4929.0</td>\n",
       "      <td>4989.0</td>\n",
       "      <td>4930.0</td>\n",
       "      <td>5038.0</td>\n",
       "      <td>4860.0</td>\n",
       "      <td>4863.0</td>\n",
       "      <td>4958.0</td>\n",
       "      <td>-126.0</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>310913.0</td>\n",
       "      <td>1541811.97</td>\n",
       "      <td>173857.0</td>\n",
       "      <td>-27013.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-01</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>4863.0</td>\n",
       "      <td>4958.0</td>\n",
       "      <td>4860.0</td>\n",
       "      <td>4982.0</td>\n",
       "      <td>4803.0</td>\n",
       "      <td>4962.0</td>\n",
       "      <td>4879.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-79.0</td>\n",
       "      <td>301561.0</td>\n",
       "      <td>1471577.58</td>\n",
       "      <td>175298.0</td>\n",
       "      <td>1441.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               ts_code  pre_close  pre_settle    open    high     low   close  \\\n",
       "trade_date                                                                      \n",
       "2021-01-27  EG2201.DCE        NaN      4360.0     NaN     NaN     NaN  4360.0   \n",
       "2021-01-28  EG2201.DCE     4360.0      4360.0  4437.0  4437.0  4437.0  4437.0   \n",
       "2021-01-29  EG2201.DCE     4437.0      4437.0  4547.0  4547.0  4462.0  4462.0   \n",
       "2021-02-01  EG2201.DCE     4462.0      4504.0  4632.0  4632.0  4615.0  4615.0   \n",
       "2021-02-02  EG2201.DCE     4615.0      4623.0  4660.0  4660.0  4605.0  4605.0   \n",
       "...                ...        ...         ...     ...     ...     ...     ...   \n",
       "2021-11-25  EG2201.DCE     5216.0      5232.0  5216.0  5353.0  5190.0  5267.0   \n",
       "2021-11-26  EG2201.DCE     5267.0      5273.0  5267.0  5270.0  5055.0  5055.0   \n",
       "2021-11-29  EG2201.DCE     5055.0      5154.0  4986.0  5076.0  4906.0  4929.0   \n",
       "2021-11-30  EG2201.DCE     4929.0      4989.0  4930.0  5038.0  4860.0  4863.0   \n",
       "2021-12-01  EG2201.DCE     4863.0      4958.0  4860.0  4982.0  4803.0  4962.0   \n",
       "\n",
       "            settle  change1  change2       vol      amount        oi   oi_chg  \n",
       "trade_date                                                                     \n",
       "2021-01-27  4360.0      0.0      0.0       0.0        0.00       0.0      NaN  \n",
       "2021-01-28  4437.0     77.0     77.0       1.0        4.44       1.0      1.0  \n",
       "2021-01-29  4504.0     25.0     67.0       2.0        9.01       1.0      0.0  \n",
       "2021-02-01  4623.0    111.0    119.0       2.0        9.25       3.0      2.0  \n",
       "2021-02-02  4641.0    -18.0     18.0       3.0       13.92       2.0     -1.0  \n",
       "...            ...      ...      ...       ...         ...       ...      ...  \n",
       "2021-11-25  5273.0     35.0     41.0  373413.0  1969348.48  203000.0  17181.0  \n",
       "2021-11-26  5154.0   -218.0   -119.0  275188.0  1418459.77  179694.0 -23306.0  \n",
       "2021-11-29  4989.0   -225.0   -165.0  327885.0  1636140.32  200870.0  21176.0  \n",
       "2021-11-30  4958.0   -126.0    -31.0  310913.0  1541811.97  173857.0 -27013.0  \n",
       "2021-12-01  4879.0      4.0    -79.0  301561.0  1471577.58  175298.0   1441.0  \n",
       "\n",
       "[204 rows x 14 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ta."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2016x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_curve(df_ma_sma['2021-09-06':])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>sma_5</th>\n",
       "      <th>sma_12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-10</th>\n",
       "      <td>5247.0</td>\n",
       "      <td>5246.4</td>\n",
       "      <td>5124.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-13</th>\n",
       "      <td>5328.0</td>\n",
       "      <td>5272.8</td>\n",
       "      <td>5162.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-14</th>\n",
       "      <td>5443.0</td>\n",
       "      <td>5305.4</td>\n",
       "      <td>5192.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-15</th>\n",
       "      <td>5422.0</td>\n",
       "      <td>5335.8</td>\n",
       "      <td>5228.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-16</th>\n",
       "      <td>5454.0</td>\n",
       "      <td>5378.8</td>\n",
       "      <td>5265.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-17</th>\n",
       "      <td>5428.0</td>\n",
       "      <td>5415.0</td>\n",
       "      <td>5299.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-22</th>\n",
       "      <td>5438.0</td>\n",
       "      <td>5437.0</td>\n",
       "      <td>5325.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-23</th>\n",
       "      <td>5552.0</td>\n",
       "      <td>5458.8</td>\n",
       "      <td>5358.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-24</th>\n",
       "      <td>5642.0</td>\n",
       "      <td>5502.8</td>\n",
       "      <td>5395.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-27</th>\n",
       "      <td>5885.0</td>\n",
       "      <td>5589.0</td>\n",
       "      <td>5445.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-28</th>\n",
       "      <td>6161.0</td>\n",
       "      <td>5735.6</td>\n",
       "      <td>5519.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-29</th>\n",
       "      <td>6055.0</td>\n",
       "      <td>5859.0</td>\n",
       "      <td>5587.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>6470.0</td>\n",
       "      <td>6042.6</td>\n",
       "      <td>5689.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-08</th>\n",
       "      <td>6384.0</td>\n",
       "      <td>6191.0</td>\n",
       "      <td>5777.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-11</th>\n",
       "      <td>6890.0</td>\n",
       "      <td>6392.0</td>\n",
       "      <td>5898.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-12</th>\n",
       "      <td>6873.0</td>\n",
       "      <td>6534.4</td>\n",
       "      <td>6019.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-13</th>\n",
       "      <td>6392.0</td>\n",
       "      <td>6601.8</td>\n",
       "      <td>6097.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-14</th>\n",
       "      <td>6597.0</td>\n",
       "      <td>6627.2</td>\n",
       "      <td>6194.916667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-15</th>\n",
       "      <td>6959.0</td>\n",
       "      <td>6742.2</td>\n",
       "      <td>6321.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-18</th>\n",
       "      <td>7432.0</td>\n",
       "      <td>6850.6</td>\n",
       "      <td>6478.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             close   sma_5       sma_12\n",
       "trade_date                             \n",
       "2021-09-10  5247.0  5246.4  5124.250000\n",
       "2021-09-13  5328.0  5272.8  5162.500000\n",
       "2021-09-14  5443.0  5305.4  5192.750000\n",
       "2021-09-15  5422.0  5335.8  5228.000000\n",
       "2021-09-16  5454.0  5378.8  5265.166667\n",
       "2021-09-17  5428.0  5415.0  5299.583333\n",
       "2021-09-22  5438.0  5437.0  5325.416667\n",
       "2021-09-23  5552.0  5458.8  5358.083333\n",
       "2021-09-24  5642.0  5502.8  5395.250000\n",
       "2021-09-27  5885.0  5589.0  5445.666667\n",
       "2021-09-28  6161.0  5735.6  5519.916667\n",
       "2021-09-29  6055.0  5859.0  5587.916667\n",
       "2021-09-30  6470.0  6042.6  5689.833333\n",
       "2021-10-08  6384.0  6191.0  5777.833333\n",
       "2021-10-11  6890.0  6392.0  5898.416667\n",
       "2021-10-12  6873.0  6534.4  6019.333333\n",
       "2021-10-13  6392.0  6601.8  6097.500000\n",
       "2021-10-14  6597.0  6627.2  6194.916667\n",
       "2021-10-15  6959.0  6742.2  6321.666667\n",
       "2021-10-18  7432.0  6850.6  6478.333333"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ma_sma['2021-09-10':].head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>pre_settle</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>settle</th>\n",
       "      <th>change1</th>\n",
       "      <th>change2</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>oi</th>\n",
       "      <th>oi_chg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>2021-11-24</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>5178.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>5289.0</td>\n",
       "      <td>5180.0</td>\n",
       "      <td>5216.0</td>\n",
       "      <td>5232.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>250381.0</td>\n",
       "      <td>1310197.68</td>\n",
       "      <td>185819.0</td>\n",
       "      <td>-5089.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>2021-11-23</td>\n",
       "      <td>5134.0</td>\n",
       "      <td>5089.0</td>\n",
       "      <td>5166.0</td>\n",
       "      <td>5248.0</td>\n",
       "      <td>5116.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>5178.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>263255.0</td>\n",
       "      <td>1363265.42</td>\n",
       "      <td>190908.0</td>\n",
       "      <td>-15226.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>2021-11-22</td>\n",
       "      <td>5091.0</td>\n",
       "      <td>4992.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5173.0</td>\n",
       "      <td>4961.0</td>\n",
       "      <td>5134.0</td>\n",
       "      <td>5089.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>331751.0</td>\n",
       "      <td>1688453.37</td>\n",
       "      <td>206134.0</td>\n",
       "      <td>-11459.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>2021-11-19</td>\n",
       "      <td>4965.0</td>\n",
       "      <td>5042.0</td>\n",
       "      <td>4965.0</td>\n",
       "      <td>5136.0</td>\n",
       "      <td>4883.0</td>\n",
       "      <td>5091.0</td>\n",
       "      <td>4992.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>-50.0</td>\n",
       "      <td>396160.0</td>\n",
       "      <td>1977926.74</td>\n",
       "      <td>217593.0</td>\n",
       "      <td>2974.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>EG2201.DCE</td>\n",
       "      <td>2021-11-18</td>\n",
       "      <td>5116.0</td>\n",
       "      <td>5101.0</td>\n",
       "      <td>5110.0</td>\n",
       "      <td>5128.0</td>\n",
       "      <td>4952.0</td>\n",
       "      <td>4965.0</td>\n",
       "      <td>5042.0</td>\n",
       "      <td>-136.0</td>\n",
       "      <td>-59.0</td>\n",
       "      <td>386237.0</td>\n",
       "      <td>1947607.52</td>\n",
       "      <td>214619.0</td>\n",
       "      <td>10862.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ts_code trade_date  pre_close  pre_settle    open    high     low  \\\n",
       "0  EG2201.DCE 2021-11-24     5200.0      5178.0  5200.0  5289.0  5180.0   \n",
       "1  EG2201.DCE 2021-11-23     5134.0      5089.0  5166.0  5248.0  5116.0   \n",
       "2  EG2201.DCE 2021-11-22     5091.0      4992.0  5000.0  5173.0  4961.0   \n",
       "3  EG2201.DCE 2021-11-19     4965.0      5042.0  4965.0  5136.0  4883.0   \n",
       "4  EG2201.DCE 2021-11-18     5116.0      5101.0  5110.0  5128.0  4952.0   \n",
       "\n",
       "    close  settle  change1  change2       vol      amount        oi   oi_chg  \n",
       "0  5216.0  5232.0     38.0     54.0  250381.0  1310197.68  185819.0  -5089.0  \n",
       "1  5200.0  5178.0    111.0     89.0  263255.0  1363265.42  190908.0 -15226.0  \n",
       "2  5134.0  5089.0    142.0     97.0  331751.0  1688453.37  206134.0 -11459.0  \n",
       "3  5091.0  4992.0     49.0    -50.0  396160.0  1977926.74  217593.0   2974.0  \n",
       "4  4965.0  5042.0   -136.0    -59.0  386237.0  1947607.52  214619.0  10862.0  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5216.0\n",
       "1      5200.0\n",
       "2      5134.0\n",
       "3      5091.0\n",
       "4      4965.0\n",
       "        ...  \n",
       "194    4605.0\n",
       "195    4615.0\n",
       "196    4462.0\n",
       "197    4437.0\n",
       "198    4360.0\n",
       "Name: close, Length: 199, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.close"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def get_df_smas(df, lenthlist):\n",
    "    df_temp = pd.DataFrame(df.close)\n",
    "    df_temp.index = df.index\n",
    "    for i in lenthlist:\n",
    "        df_temp['sma_'+ str(i)] = ta.SMA(df['close'], i)\n",
    "    \n",
    "    return df_temp\n",
    "\n",
    "def get_day_bbands(df):\n",
    "    df_bbands = pd.DataFrame(df['close'])\n",
    "    df_bbands['upper'], df_bbands['middle'], df_bbands['lower'] = ta.BBANDS(\n",
    "                df.close, \n",
    "                timeperiod=10,\n",
    "                # number of non-biased standard deviations from the mean\n",
    "                nbdevup= 2,\n",
    "                nbdevdn= 2,\n",
    "                # Moving average type: simple moving average here\n",
    "                matype=0)\n",
    "    df_bbands['signal_B'] = df_bbands['close'] <= df_bbands['lower']\n",
    "    df_bbands['signal_S'] = df_bbands['close'] >= df_bbands['upper']\n",
    "    return df_bbands\n",
    "\n",
    "\n",
    "def plot_curve(plot_dict):\n",
    "    \"\"\"\n",
    "    plot the net asset value curve\n",
    "    :param plot_dict: type--dict, {label name (str): time series data (pd.Series)}\n",
    "    :return: None\n",
    "    \"\"\"\n",
    "    import matplotlib.pyplot as plt\n",
    "    fig, ax = plt.subplots(figsize=(28, 8))\n",
    "    fig.patch.set_facecolor('white')\n",
    "    transparency_level = 1.\n",
    "    \n",
    "    for _ts_label, _ts_curve in plot_dict.items():\n",
    "        _ts_curve.plot(ax=ax, lw=1.5, label=_ts_label, alpha=transparency_level)\n",
    "    \n",
    "    ax.legend(loc='upper left', shadow=True)    \n",
    "    plt.grid()\n",
    "    plt.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import talib as ta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ta.MACD?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_recent = df[['open', 'high','low','close','change1']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\vnstudio\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "c:\\vnstudio\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n",
      "c:\\vnstudio\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "c:\\vnstudio\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "df_recent['close-open'] = df_recent['close'] - df_recent['open']\n",
    "df_recent['high-low'] = df_recent['high'] - df_recent['low']\n",
    "df_recent['sell'] = df_recent['high'] - df_recent['close']\n",
    "df_recent['buy'] = df_recent['close'] - df_recent['low']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfmacd = ta.MACD(df.close, 26, 13, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='trade_date'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.close.plot(figsize=(16,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='trade_date'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "macd.plot(figsize=(16,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "macd, dea, hist = dfmacd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='trade_date'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dea.plot(figsize=(16,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='trade_date'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist.plot(figsize=(16,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\vnstudio\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df_recent['dayofweek'] = df_recent.index.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>close-open</th>\n",
       "      <th>high-low</th>\n",
       "      <th>sell</th>\n",
       "      <th>buy</th>\n",
       "      <th>dayofweek</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-01-27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4360.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-28</th>\n",
       "      <td>4437.0</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>4437.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-01-29</th>\n",
       "      <td>4547.0</td>\n",
       "      <td>4547.0</td>\n",
       "      <td>4462.0</td>\n",
       "      <td>4462.0</td>\n",
       "      <td>-85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-01</th>\n",
       "      <td>4632.0</td>\n",
       "      <td>4632.0</td>\n",
       "      <td>4615.0</td>\n",
       "      <td>4615.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-02</th>\n",
       "      <td>4660.0</td>\n",
       "      <td>4660.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>-55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-13</th>\n",
       "      <td>4806.0</td>\n",
       "      <td>4956.0</td>\n",
       "      <td>4773.0</td>\n",
       "      <td>4931.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>183.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-14</th>\n",
       "      <td>4880.0</td>\n",
       "      <td>4990.0</td>\n",
       "      <td>4836.0</td>\n",
       "      <td>4988.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-15</th>\n",
       "      <td>4987.0</td>\n",
       "      <td>4987.0</td>\n",
       "      <td>4878.0</td>\n",
       "      <td>4909.0</td>\n",
       "      <td>-78.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-16</th>\n",
       "      <td>4909.0</td>\n",
       "      <td>4983.0</td>\n",
       "      <td>4881.0</td>\n",
       "      <td>4968.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-17</th>\n",
       "      <td>4967.0</td>\n",
       "      <td>5030.0</td>\n",
       "      <td>4937.0</td>\n",
       "      <td>4961.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>216 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              open    high     low   close  close-open  high-low  sell    buy  \\\n",
       "trade_date                                                                      \n",
       "2021-01-27     NaN     NaN     NaN  4360.0         NaN       NaN   NaN    NaN   \n",
       "2021-01-28  4437.0  4437.0  4437.0  4437.0         0.0       0.0   0.0    0.0   \n",
       "2021-01-29  4547.0  4547.0  4462.0  4462.0       -85.0      85.0  85.0    0.0   \n",
       "2021-02-01  4632.0  4632.0  4615.0  4615.0       -17.0      17.0  17.0    0.0   \n",
       "2021-02-02  4660.0  4660.0  4605.0  4605.0       -55.0      55.0  55.0    0.0   \n",
       "...            ...     ...     ...     ...         ...       ...   ...    ...   \n",
       "2021-12-13  4806.0  4956.0  4773.0  4931.0       125.0     183.0  25.0  158.0   \n",
       "2021-12-14  4880.0  4990.0  4836.0  4988.0       108.0     154.0   2.0  152.0   \n",
       "2021-12-15  4987.0  4987.0  4878.0  4909.0       -78.0     109.0  78.0   31.0   \n",
       "2021-12-16  4909.0  4983.0  4881.0  4968.0        59.0     102.0  15.0   87.0   \n",
       "2021-12-17  4967.0  5030.0  4937.0  4961.0        -6.0      93.0  69.0   24.0   \n",
       "\n",
       "            dayofweek  \n",
       "trade_date             \n",
       "2021-01-27          2  \n",
       "2021-01-28          3  \n",
       "2021-01-29          4  \n",
       "2021-02-01          0  \n",
       "2021-02-02          1  \n",
       "...               ...  \n",
       "2021-12-13          0  \n",
       "2021-12-14          1  \n",
       "2021-12-15          2  \n",
       "2021-12-16          3  \n",
       "2021-12-17          4  \n",
       "\n",
       "[216 rows x 9 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_recent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df_recent[df_recent['dayofweek'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('总共:', 40, '涨', 20, 19, 1)\n",
      "('总共:', 42, '涨', 16, 25, 1)\n",
      "('总共:', 44, '涨', 18, 22, 3)\n",
      "('总共:', 45, '涨', 25, 17, 3)\n",
      "('总共:', 45, '涨', 23, 22, 0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\vnstudio\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "for week in range(0,5):\n",
    "    print(get_up_down_count(df_recent,week))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_recent.to_csv('D:\\Data\\eg2201.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     45.000000\n",
       "mean     182.533333\n",
       "std      131.230297\n",
       "min       28.000000\n",
       "25%       93.000000\n",
       "50%      132.000000\n",
       "75%      232.000000\n",
       "max      568.000000\n",
       "Name: high-low, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df_recent[df_recent['dayofweek'] == 4]\n",
    "df1['high-low'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>change1</th>\n",
       "      <th>close-open</th>\n",
       "      <th>high-low</th>\n",
       "      <th>sell</th>\n",
       "      <th>buy</th>\n",
       "      <th>dayofweek</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-02-02</th>\n",
       "      <td>4660.0</td>\n",
       "      <td>4660.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>4605.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>-55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-09</th>\n",
       "      <td>4580.0</td>\n",
       "      <td>4581.0</td>\n",
       "      <td>4580.0</td>\n",
       "      <td>4581.0</td>\n",
       "      <td>-49.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-02-23</th>\n",
       "      <td>4887.0</td>\n",
       "      <td>4984.0</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>4869.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>184.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-02</th>\n",
       "      <td>5060.0</td>\n",
       "      <td>5070.0</td>\n",
       "      <td>4822.0</td>\n",
       "      <td>4832.0</td>\n",
       "      <td>-343.0</td>\n",
       "      <td>-228.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-09</th>\n",
       "      <td>5074.0</td>\n",
       "      <td>5089.0</td>\n",
       "      <td>4950.0</td>\n",
       "      <td>4950.0</td>\n",
       "      <td>-195.0</td>\n",
       "      <td>-124.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-16</th>\n",
       "      <td>4955.0</td>\n",
       "      <td>5016.0</td>\n",
       "      <td>4897.0</td>\n",
       "      <td>4944.0</td>\n",
       "      <td>-44.0</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-23</th>\n",
       "      <td>4898.0</td>\n",
       "      <td>4898.0</td>\n",
       "      <td>4806.0</td>\n",
       "      <td>4886.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-03-30</th>\n",
       "      <td>4845.0</td>\n",
       "      <td>4944.0</td>\n",
       "      <td>4845.0</td>\n",
       "      <td>4944.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-06</th>\n",
       "      <td>5030.0</td>\n",
       "      <td>5030.0</td>\n",
       "      <td>5020.0</td>\n",
       "      <td>5030.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-13</th>\n",
       "      <td>4785.0</td>\n",
       "      <td>4791.0</td>\n",
       "      <td>4695.0</td>\n",
       "      <td>4766.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-20</th>\n",
       "      <td>4872.0</td>\n",
       "      <td>4872.0</td>\n",
       "      <td>4757.0</td>\n",
       "      <td>4811.0</td>\n",
       "      <td>-60.0</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-27</th>\n",
       "      <td>4644.0</td>\n",
       "      <td>4749.0</td>\n",
       "      <td>4640.0</td>\n",
       "      <td>4710.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-11</th>\n",
       "      <td>5172.0</td>\n",
       "      <td>5172.0</td>\n",
       "      <td>4995.0</td>\n",
       "      <td>5060.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>-112.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-18</th>\n",
       "      <td>4976.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>4976.0</td>\n",
       "      <td>5053.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-25</th>\n",
       "      <td>4920.0</td>\n",
       "      <td>4981.0</td>\n",
       "      <td>4888.0</td>\n",
       "      <td>4919.0</td>\n",
       "      <td>-28.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>5060.0</td>\n",
       "      <td>5152.0</td>\n",
       "      <td>5050.0</td>\n",
       "      <td>5113.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-08</th>\n",
       "      <td>4885.0</td>\n",
       "      <td>4901.0</td>\n",
       "      <td>4808.0</td>\n",
       "      <td>4821.0</td>\n",
       "      <td>-152.0</td>\n",
       "      <td>-64.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-15</th>\n",
       "      <td>4879.0</td>\n",
       "      <td>4910.0</td>\n",
       "      <td>4820.0</td>\n",
       "      <td>4845.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-34.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-22</th>\n",
       "      <td>4755.0</td>\n",
       "      <td>4910.0</td>\n",
       "      <td>4754.0</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>156.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>146.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-29</th>\n",
       "      <td>5036.0</td>\n",
       "      <td>5076.0</td>\n",
       "      <td>4988.0</td>\n",
       "      <td>5021.0</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-06</th>\n",
       "      <td>5163.0</td>\n",
       "      <td>5260.0</td>\n",
       "      <td>5163.0</td>\n",
       "      <td>5213.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-13</th>\n",
       "      <td>4937.0</td>\n",
       "      <td>5160.0</td>\n",
       "      <td>4937.0</td>\n",
       "      <td>5145.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>223.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-20</th>\n",
       "      <td>5240.0</td>\n",
       "      <td>5281.0</td>\n",
       "      <td>5123.0</td>\n",
       "      <td>5175.0</td>\n",
       "      <td>-63.0</td>\n",
       "      <td>-65.0</td>\n",
       "      <td>158.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-27</th>\n",
       "      <td>5247.0</td>\n",
       "      <td>5290.0</td>\n",
       "      <td>5170.0</td>\n",
       "      <td>5203.0</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>-44.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-03</th>\n",
       "      <td>5185.0</td>\n",
       "      <td>5209.0</td>\n",
       "      <td>5110.0</td>\n",
       "      <td>5135.0</td>\n",
       "      <td>-105.0</td>\n",
       "      <td>-50.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-10</th>\n",
       "      <td>5218.0</td>\n",
       "      <td>5251.0</td>\n",
       "      <td>5150.0</td>\n",
       "      <td>5185.0</td>\n",
       "      <td>-50.0</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-17</th>\n",
       "      <td>5042.0</td>\n",
       "      <td>5063.0</td>\n",
       "      <td>4986.0</td>\n",
       "      <td>4991.0</td>\n",
       "      <td>-61.0</td>\n",
       "      <td>-51.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-24</th>\n",
       "      <td>4980.0</td>\n",
       "      <td>5004.0</td>\n",
       "      <td>4931.0</td>\n",
       "      <td>4941.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>-39.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-31</th>\n",
       "      <td>5025.0</td>\n",
       "      <td>5043.0</td>\n",
       "      <td>4980.0</td>\n",
       "      <td>5008.0</td>\n",
       "      <td>-57.0</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-07</th>\n",
       "      <td>5210.0</td>\n",
       "      <td>5285.0</td>\n",
       "      <td>5185.0</td>\n",
       "      <td>5280.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-14</th>\n",
       "      <td>5328.0</td>\n",
       "      <td>5469.0</td>\n",
       "      <td>5309.0</td>\n",
       "      <td>5443.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>134.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-28</th>\n",
       "      <td>5900.0</td>\n",
       "      <td>6234.0</td>\n",
       "      <td>5900.0</td>\n",
       "      <td>6161.0</td>\n",
       "      <td>304.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>334.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-12</th>\n",
       "      <td>6928.0</td>\n",
       "      <td>7118.0</td>\n",
       "      <td>6817.0</td>\n",
       "      <td>6873.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>-55.0</td>\n",
       "      <td>301.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-19</th>\n",
       "      <td>7500.0</td>\n",
       "      <td>7566.0</td>\n",
       "      <td>7137.0</td>\n",
       "      <td>7324.0</td>\n",
       "      <td>215.0</td>\n",
       "      <td>-176.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-26</th>\n",
       "      <td>6280.0</td>\n",
       "      <td>6300.0</td>\n",
       "      <td>5943.0</td>\n",
       "      <td>6071.0</td>\n",
       "      <td>-209.0</td>\n",
       "      <td>-209.0</td>\n",
       "      <td>357.0</td>\n",
       "      <td>229.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-02</th>\n",
       "      <td>5696.0</td>\n",
       "      <td>5820.0</td>\n",
       "      <td>5524.0</td>\n",
       "      <td>5710.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-09</th>\n",
       "      <td>5480.0</td>\n",
       "      <td>5558.0</td>\n",
       "      <td>5361.0</td>\n",
       "      <td>5410.0</td>\n",
       "      <td>-80.0</td>\n",
       "      <td>-70.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-16</th>\n",
       "      <td>5173.0</td>\n",
       "      <td>5250.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>5208.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-23</th>\n",
       "      <td>5166.0</td>\n",
       "      <td>5248.0</td>\n",
       "      <td>5116.0</td>\n",
       "      <td>5200.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-11-30</th>\n",
       "      <td>4930.0</td>\n",
       "      <td>5038.0</td>\n",
       "      <td>4860.0</td>\n",
       "      <td>4863.0</td>\n",
       "      <td>-126.0</td>\n",
       "      <td>-67.0</td>\n",
       "      <td>178.0</td>\n",
       "      <td>175.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-07</th>\n",
       "      <td>4730.0</td>\n",
       "      <td>4881.0</td>\n",
       "      <td>4729.0</td>\n",
       "      <td>4844.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-14</th>\n",
       "      <td>4880.0</td>\n",
       "      <td>4990.0</td>\n",
       "      <td>4836.0</td>\n",
       "      <td>4988.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              open    high     low   close  change1  close-open  high-low  \\\n",
       "trade_date                                                                  \n",
       "2021-02-02  4660.0  4660.0  4605.0  4605.0    -18.0       -55.0      55.0   \n",
       "2021-02-09  4580.0  4581.0  4580.0  4581.0    -49.0         1.0       1.0   \n",
       "2021-02-23  4887.0  4984.0  4800.0  4869.0     81.0       -18.0     184.0   \n",
       "2021-03-02  5060.0  5070.0  4822.0  4832.0   -343.0      -228.0     248.0   \n",
       "2021-03-09  5074.0  5089.0  4950.0  4950.0   -195.0      -124.0     139.0   \n",
       "2021-03-16  4955.0  5016.0  4897.0  4944.0    -44.0       -11.0     119.0   \n",
       "2021-03-23  4898.0  4898.0  4806.0  4886.0     28.0       -12.0      92.0   \n",
       "2021-03-30  4845.0  4944.0  4845.0  4944.0     83.0        99.0      99.0   \n",
       "2021-04-06  5030.0  5030.0  5020.0  5030.0     39.0         0.0      10.0   \n",
       "2021-04-13  4785.0  4791.0  4695.0  4766.0     -5.0       -19.0      96.0   \n",
       "2021-04-20  4872.0  4872.0  4757.0  4811.0    -60.0       -61.0     115.0   \n",
       "2021-04-27  4644.0  4749.0  4640.0  4710.0     34.0        66.0     109.0   \n",
       "2021-05-11  5172.0  5172.0  4995.0  5060.0      7.0      -112.0     177.0   \n",
       "2021-05-18  4976.0  5100.0  4976.0  5053.0     30.0        77.0     124.0   \n",
       "2021-05-25  4920.0  4981.0  4888.0  4919.0    -28.0        -1.0      93.0   \n",
       "2021-06-01  5060.0  5152.0  5050.0  5113.0     82.0        53.0     102.0   \n",
       "2021-06-08  4885.0  4901.0  4808.0  4821.0   -152.0       -64.0      93.0   \n",
       "2021-06-15  4879.0  4910.0  4820.0  4845.0      8.0       -34.0      90.0   \n",
       "2021-06-22  4755.0  4910.0  4754.0  4900.0     66.0       145.0     156.0   \n",
       "2021-06-29  5036.0  5076.0  4988.0  5021.0    -19.0       -15.0      88.0   \n",
       "2021-07-06  5163.0  5260.0  5163.0  5213.0     57.0        50.0      97.0   \n",
       "2021-07-13  4937.0  5160.0  4937.0  5145.0     29.0       208.0     223.0   \n",
       "2021-07-20  5240.0  5281.0  5123.0  5175.0    -63.0       -65.0     158.0   \n",
       "2021-07-27  5247.0  5290.0  5170.0  5203.0    -23.0       -44.0     120.0   \n",
       "2021-08-03  5185.0  5209.0  5110.0  5135.0   -105.0       -50.0      99.0   \n",
       "2021-08-10  5218.0  5251.0  5150.0  5185.0    -50.0       -33.0     101.0   \n",
       "2021-08-17  5042.0  5063.0  4986.0  4991.0    -61.0       -51.0      77.0   \n",
       "2021-08-24  4980.0  5004.0  4931.0  4941.0     20.0       -39.0      73.0   \n",
       "2021-08-31  5025.0  5043.0  4980.0  5008.0    -57.0       -17.0      63.0   \n",
       "2021-09-07  5210.0  5285.0  5185.0  5280.0    102.0        70.0     100.0   \n",
       "2021-09-14  5328.0  5469.0  5309.0  5443.0    133.0       115.0     160.0   \n",
       "2021-09-28  5900.0  6234.0  5900.0  6161.0    304.0       261.0     334.0   \n",
       "2021-10-12  6928.0  7118.0  6817.0  6873.0    143.0       -55.0     301.0   \n",
       "2021-10-19  7500.0  7566.0  7137.0  7324.0    215.0      -176.0     429.0   \n",
       "2021-10-26  6280.0  6300.0  5943.0  6071.0   -209.0      -209.0     357.0   \n",
       "2021-11-02  5696.0  5820.0  5524.0  5710.0     99.0        14.0     296.0   \n",
       "2021-11-09  5480.0  5558.0  5361.0  5410.0    -80.0       -70.0     197.0   \n",
       "2021-11-16  5173.0  5250.0  5100.0  5208.0    -36.0        35.0     150.0   \n",
       "2021-11-23  5166.0  5248.0  5116.0  5200.0    111.0        34.0     132.0   \n",
       "2021-11-30  4930.0  5038.0  4860.0  4863.0   -126.0       -67.0     178.0   \n",
       "2021-12-07  4730.0  4881.0  4729.0  4844.0     50.0       114.0     152.0   \n",
       "2021-12-14  4880.0  4990.0  4836.0  4988.0    119.0       108.0     154.0   \n",
       "\n",
       "             sell    buy  dayofweek  \n",
       "trade_date                           \n",
       "2021-02-02   55.0    0.0          1  \n",
       "2021-02-09    0.0    1.0          1  \n",
       "2021-02-23  115.0   69.0          1  \n",
       "2021-03-02  238.0   10.0          1  \n",
       "2021-03-09  139.0    0.0          1  \n",
       "2021-03-16   72.0   47.0          1  \n",
       "2021-03-23   12.0   80.0          1  \n",
       "2021-03-30    0.0   99.0          1  \n",
       "2021-04-06    0.0   10.0          1  \n",
       "2021-04-13   25.0   71.0          1  \n",
       "2021-04-20   61.0   54.0          1  \n",
       "2021-04-27   39.0   70.0          1  \n",
       "2021-05-11  112.0   65.0          1  \n",
       "2021-05-18   47.0   77.0          1  \n",
       "2021-05-25   62.0   31.0          1  \n",
       "2021-06-01   39.0   63.0          1  \n",
       "2021-06-08   80.0   13.0          1  \n",
       "2021-06-15   65.0   25.0          1  \n",
       "2021-06-22   10.0  146.0          1  \n",
       "2021-06-29   55.0   33.0          1  \n",
       "2021-07-06   47.0   50.0          1  \n",
       "2021-07-13   15.0  208.0          1  \n",
       "2021-07-20  106.0   52.0          1  \n",
       "2021-07-27   87.0   33.0          1  \n",
       "2021-08-03   74.0   25.0          1  \n",
       "2021-08-10   66.0   35.0          1  \n",
       "2021-08-17   72.0    5.0          1  \n",
       "2021-08-24   63.0   10.0          1  \n",
       "2021-08-31   35.0   28.0          1  \n",
       "2021-09-07    5.0   95.0          1  \n",
       "2021-09-14   26.0  134.0          1  \n",
       "2021-09-28   73.0  261.0          1  \n",
       "2021-10-12  245.0   56.0          1  \n",
       "2021-10-19  242.0  187.0          1  \n",
       "2021-10-26  229.0  128.0          1  \n",
       "2021-11-02  110.0  186.0          1  \n",
       "2021-11-09  148.0   49.0          1  \n",
       "2021-11-16   42.0  108.0          1  \n",
       "2021-11-23   48.0   84.0          1  \n",
       "2021-11-30  175.0    3.0          1  \n",
       "2021-12-07   37.0  115.0          1  \n",
       "2021-12-14    2.0  152.0          1  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_up_down_count(df, weekday):\n",
    "    df1 = df_recent[df_recent['dayofweek'] == weekday]\n",
    "    return('总共:',len(df1),'涨',len(df1[df['close-open'] > 0]), len(df1[df['close-open'] < 0]), len(df1[df['close-open'] == 0]))\n",
    "    "
   ]
  }
 ],
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